PACS: a prerequisite for image fusion in nuclear medicine

International Congress Series 1230 (2001) 767 – 772

PACS: a prerequisite for image fusion in nuclear medicine T. Kauppinena,*, H. Pohjonenb, R. Laakkonenb, O. Sipila¨b, P. Nikkinena, I. Sippo-Tujunena, P. Va¨lima¨kic, M. Kortesniemib, S. Savolainenb, J. Kinnunenb a

Division of Nuclear Medicine, HUCH-Laboratory Diagnostics, Helsinki University Central Hospital, Haartmaninkatu 4, P.O. Box 340, FIN-00029 Helsinki, Finland b Department of Radiology, Helsinki University Central Hospital, P.O. Box 340, FIN-00029 Helsinki, Finland c Division of Clinical Neurophysiology, HUCH-Laboratory Diagnostics, Helsinki University Central Hospital, P.O. Box 340, FIN-00029 Helsinki, Finland

Abstract Digital images of nuclear medicine (NM) are produced by a gamma camera. Compared with radiological images, the resolution of NM images is low. However, they reveal regional functional differences, whereas radiological images show high-resolution anatomical details. Combination of anatomical and functional image data from the same part of the body is important and can enhance the understanding of functional abnormalities. The purpose of this study was to introduce a picture archiving and communication system (PACS) for practical clinical use in the divisions of nuclear medicine. The effect of PACS on customizing image fusion in nuclear medicine was also evaluated. For routine use of image fusion, it is essential to have a fast image network for transferring images from different modalities and a PACS for storing them. It is also essential to have a common commitment to Digital Imaging and Communications in Medicine (Dicom) standard. Openarchitecture PACS seems to remove the remaining difficulties in customising image fusion. The PACS introduced in the hospital district will be one of the largest in the world when completed in 2003. D 2001 Elsevier Science B.V. All rights reserved. Keywords: Pacs; Fusion; Registration; Nuclear medicine

*

Corresponding author. Tel.: +358-9-471-60327; fax: +358-9-471-76678. E-mail address: [email protected] (T. Kauppinen).

0531-5131/01/$ – see front matter D 2001 Elsevier Science B.V. All rights reserved. PII: S 0 5 3 1 - 5 1 3 1 ( 0 1 ) 0 0 1 3 1 - 5

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1. Introduction In nuclear medicine (NM), digital gamma camera images are produced. Compared with radiology, the resolution of NM images is low. However, NM images reveal regional functional differences, whereas radiological images show high-resolution anatomical details. Combination of anatomical and functional image data from the same part of the body is important and can enhance the understanding of functional abnormalities. Efficient routine use of image fusion requires a picture archiving and communication system (PACS) to provide storage of image data in a standard digital format, a high-speed image network as well as an accurate and reliable image fusion software. In nuclear medicine, patients can be followed for several years and the same study can be repeated every 6 or 12 months. Therefore, it is important to have the previous NM images for comparison. NM studies can be performed in any of the NM units of the hospital district, and the previous images including the related graphical data, like regions of interest, should preferably be available in any other unit as well. In the early 1990s, a method for registration and visualization of brain single photon emission tomography (SPET) and magnetic resonance (MR) images using external skin markers was developed ‘‘in house’’. Today, it is also possible to use commercial fusion software. The purpose of this study was to introduce a PACS for practical clinical use in the divisions of nuclear medicine as a part of the regional PACS. The effect of PACS on customizing image fusion in the divisions of nuclear medicine was also evaluated; image fusion has been applied in selected clinical brain images since 1993.

2. Material and methods 2.1. HUSpacs architecture and networking The Hospital District of Helsinki and Uusimaa (HUS) has a PAC system called HUSpacs gradually connecting all the 21 radiological departments with the total amount of 800.000 examinations (20 TB) per year by the end of year 2003. HUSpacs will be one of the largest PAC systems in the world. According to Finnish legislation, images have to be stored for 20 years. The architecture of HUSpacs is based on local online archives (RAID, redundant array of inexpensive disks) and a regional image database. The capacity of online archives is approximately from 1 to 2 years of image data. Each radiological department has its own short-term archive, whereas long-term archiving and back-up archiving are centralized. Interfacing PACS and RIS is essential to enable pre-fetching of images and definition of work lists to imaging modalities (see Fig. 1). RIS/PACS-integration has been carried out using HL7 standard and two integration platforms. There is also a separate integration platform for routing of HL7 messages. Because of its centralized nature, HUSpacs requires a reliable and efficient network (HUSnet). The most important requirements for both the local and frame network are round-the-clock network control and management, redundant topology, high capacity as

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Fig. 1. The PACS/RIS interface.

well as Quality of Service (QoS) and Class of service (CoS) properties. The frame network connecting the different campus areas is based on ATM technology and all the critical servers are connected directly to the backbone switches. Each campus area has its own 40 Mbps connection except HUCH-campus (Helsinki University Central Hospital), which is the largest one, having 155 Mbps connection. All physical links and active components are doubled. The vendor providing the services of the frame network has committed to the usability of 99.98% meaning about 1.7 h down-time per year. The number of users in a particular campus has to be taken into account when estimating the total effect of down-time. One of the main requirements was to guarantee QoS properties to time and mission critical applications including video conferencing and HUSpacs. These applications can be offered for special services even if the load of the network is remarkable. Non-critical services have to be restricted at the same time. 2.2. Data security Data security is specially paid attention to in protecting patient confidential information. Every user has his own unique user ID and a secret password and all user activities will be logged. This does not only concern image transfer but also image alterations and searching criteria. It should also be noted that viewing images is revealing of data and will be logged. For all logged transactions, the time and date of the activities is also logged. According to Finnish legislation, the patient has the right to check who has used information concerning him and what for.

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Patient permission is always needed when transferring data to another organisation. This permission is stored in a reference database, which is a regional information system containing references to patient information that is stored in different organisational databases (‘‘patient information directory’’). Later this year, user authentication will be performed using smart cards. Furthermore, instead of validating user logons against user profiles stored in the PACS database, we will utilise separate server containing all users, their roles and role profiles. In the future, radiological reports are also digitally signed using this smart card. 2.3. NM images NM images are produced in two campus areas in three nuclear medicine divisions with seven gamma cameras. Both campus areas have their own replicating hard disk systems consisting of 3  60 GB disk space. All the data (approximately 10 GB per year) is transferred automatically to the Hermes2 global online database in a DICOM NM format. It is also possible to transfer images acquired by other modalities (e.g. MRI, CT) to this database for image fusion. The next step is to archive this data to the regional HUSpacs database. 2.4. Image fusion We have developed a software for registering and displaying multimodal image data [1]. The SPET images are converted to the Interfile format before transferring with the TCP/IP protocol. The algorithm is based on a non-iterative least square method using singular value decomposition and a 3  3 covariance matrix of the centroid-subtracted position vectors in the two coordinate systems. There are two display modes, 2- and 3dimensional, combining registered data from the two modalities [1,2]. This method has been applied to the registration of MR images and brain perfusion SPET images from patients suffering from cerebral infarction, herpes simplex virus encephalitis, epilepsy and neuronal ceroid-lipofuscinoses. SPET-MRI fusion imaging has also been used for studying striatal dopamine transporters with the 123I-bCIT ligand in Parkinson’s disease. Since 1996, a triple-head Picker Prism 3000XP gamma camera equipped with lowenergy ultra-high resolution fan-beam collimator has been used for multimodal brain SPET studies. MR imaging was usually performed with a 1.5 T Siemens Magnetom Vision. T1 weighted sagittal slices were acquired using a 3D MPRAGE imaging sequence. For pre-surgical epilepsy patients, a simultaneous transmission –emission imaging with Gd-153 source and triple-head gamma camera imaging can be applied for co-registration of ictal and interictal brain perfusion SPET, MRI and electroencephalography [3]. In recent years, advances in fusion software and hardware have reduced the processing time required and therefore enabled multimodal imaging to be performed in daily clinical practice. The Hermes2 workstations are equipped with sophisticated multimodality software and appropriate Dicom service classes: it has been perceived to be an effective tool in image registration. External or internal landmarks or an automatic registration methods can be used for image fusion. For comparison of ictal with interictal SPET in patients with epilepsy, automatic registration methods have been particularly useful. In the landmark-

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based registration algorithm, a transformation minimising the error between the two sets of landmarks is defined. It is possible to use constrained scaling, when the scaling parameters are not changed or constrained X/Y ratio, when the scaling parameters can be changed but a constant ratio between X and Y is retained. It is also possible to adjust the parameters freely. Automatic registration can be performed using several algorithms with different length and accuracy of the computation. It has been demonstrated that registration based on count differences, minimising the sum of absolute count differences between all the voxels, is especially feasible when two SPET studies should be registered [4]. Maximisation of mutual information is useful for registration of SPET and MRI. The statistical dependence between the image intensities of corresponding voxels in both image sets is defined and it is assumed to be maximal if the images are geometrically aligned [5,6].

3. Results After introducing HUSpacs in the divisions of NM, anatomical studies can be retrieved by the standard Dicom query/retrieve function from the HUSpacs database without any separate image request to the MRI unit. Corresponding anatomical studies can be fetched easily and fast via network from the central database (Fig. 1). The SPET study can be registered to a previous anatomical or functional study as well. At first, we are offering fusion imaging as a separate product on the price list of the divisions of NM. PACS is also useful when there is temporarily no physician available in a NM unit or when a NM physician needs a second opinion from a physician sitting in another unit. With PACS and the patient’s permission, the whole imaging history of the patient is available. The organ or anatomy based pre-fetching — commonly used in radiology — was, however, found impractical in NM: the organ can be the whole body, e.g. in bone scans.

Fig. 2. Striatal MR and SPET images registered by using external markers and the in-house developed registering software (on the left). On the right: MR image has been fused with SPET. Registration has been done by Hermes2 software using automatic registering.

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It has been shown by phantom experiments and simulations that registration accuracy with our software does not limit the quantitative analysis of SPET, when the spatial resolution of SPET is taken into account [2]. Six external markers have been found to be sufficient for clinical studies. It is essential, however, that they are distributed evenly throughout the imaged volume [2]. Automatic registration based on mutual information or normalised mutual information is a reliable and accurate algorithm in registration of SPET and MRI. When registering two SPET studies, the count difference algorithm [4] was found to be better (Fig. 2).

4. Discussion and conclusions Not only anatomical and functional images but also biosignals are important data to be stored regionally and displayed with image data. Most of the neurophysiological studies in HUS including electroencephalogram and evoked potential studies are produced and processed in a digital form. One of the major restrictions is, however, the variability of the digital data formats. Today, there is no Dicom information object definition for neurophysiological studies and the devices of different manufacturers produce the proprietary data in different formats incompatible with each other. However, the division is already utilizing the RIS system. In addition, archiving of the studies will be gradually transferred from the division’s own servers to the archiving servers of the PAC system. Combination of anatomical and functional image data from the same part of the body is important and can enhance the understanding of functional abnormalities. Hence, image fusion is important in clinical work. We found that PACS is essential in customizing image fusion: registration can be performed faster and easier than before. It is also important to have a common commitment to Dicom standard.

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